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Intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Zihao Liu Yu Zhu Chengkun Zhang Qinghai Provincial Laboratory for Intelligent Computing and Application Qinghai University Xining Qinghai China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

To address the issues of inadequate feature extraction, complicated models, and a large number of network parameters, this paper proposes a change detection method for high-resolution remote sensing images based on UNet network. Firstly, Resnet50 network is selected in the encoder part to obtain deeper features, avoid gradient disappearance and reduce training time. The scSE attention module structure is introduced in the skip connection. This module enhances the network's ability to prioritize the important channels and mine the local information better. Then, in order to improve the segmentation accuracy and obtain the context information better, atrous spatial pyramid pool is carried out in the deepest feature layer of the encoder to enrich the representation of information and obtain the multi-scale information of the target. Finally, the combination of Focal loss function and BCE loss weight was presented with improving model learning strategy. On the LEVIR-CD and CDD datasets, it is shown that proposed method has improved precision rate, recall rate and F1, and the number of model parameters is 5.37M, which is superior to other commonly used change detection networks.

关键词： Training Human computer interaction Accuracy Image resolution change detection algorithms Feature extraction Sensors Remote sensing Residual neural networks Context modeling

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Detecting China's Urban Expansion Over the Past Three Decades Using Nighttime Light Data

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2014年第10期7卷 4095-4106页

作者： Xiao, Pengfeng Wang, Xiaohui Feng, Xuezhi Zhang, Xueliang Yang, Yongke Nanjing Univ Jiangsu Prov Key Lab Geog Informat Sci & Technol Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Key Lab Satellite Mapping Technol & Applicat State Adm Surveying Mapping & Geoinformat China Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Dept Geog Informat Sci Nanjing 210023 Jiangsu Peoples R China

China has experienced a rapid urban expansion over the past three decades because of its accelerated economic growth. In this study, we detected and analyzed the urban expansion of China during this period using multi-temporal Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) nighttime light data and multi-source Normalized Difference Vegetation Index (NDVI) data. First, an intercalibration was performed to improve the continuity and comparability of the nighttime light data from 1992 to 2010. The nighttime light and NDVI data were then subjected to a local support vector machine (SVM) based region-growing method to extract the urban areas from 1992 to 2010. The urban areas from 1981 to 1991 were identified using the areas in 1992 and NDVI data, based on the hypothesis that China's urban expansion continued during this period. Finally, the extracted time-series urban maps were validated with Landsat images. The proposed local SVM-based region-growing method performed better than a local thresholding method and a global SVM-based region-growing method according to visual and quantitative comparisons of the urban boundaries and areas. We also analyzed the expansion rates to understand the dynamics of the urban areas in China and in its seven economic regions. In particular, the urban expansion patterns were investigated in three typical urban agglomerations, i.e., Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta. The proposed urban expansion direction, urban expansion intensity, and relative ratio of urban expansion demonstrated the regional variation among the three urban agglomerations.

关键词： change detection algorithms land surface remote sensing support vector machines (SVMs) urban areas

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A SYSTEM FOR SEQUENTIAL STEP detection WITH APPLICATION TO VIDEO IMAGE-PROCESSING

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 1992年第4期39卷 277-284页

作者： MALONE, JA SMITH, LM Center for Laser Applications University of Tennessee Space Institute Tullahoma TN USA

A method for detecting the occurrence of an abrupt steplike change in a time sequence of video images is presented. A single-pole recursive high-pass filter cascaded with a moving average filter processes the input data to remove the quiescent background level and accumulate a sustained change in amplitude. The absolute value of the output is compared to a threshold to decide whether a steplike change in signal amplitude has occurred. It is shown that, for a given cutoff frequency of the high-pass filter, an optimal value exists for the number of terms in the moving average. Considerations for implementation of the algorithm on practical image processors are discussed. The results of numerical and laboratory experiments are presented that verify the effectiveness of the method.

关键词： Image processing Filters change detection algorithms Aerodynamics Monitoring Signal processing algorithms Infrared detectors Digital signal processing Image analysis Image sequences

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A Unified Framework for Multimodal Submodular Integrated Circuits Trojan detection

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2011年第1期6卷 162-174页

作者： Koushanfar, Farinaz Mirhoseini, Azalia Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA

This paper presents a unified formal framework for integrated circuits (ICs) Trojan detection that can simultaneously employ multiple noninvasive side-channel measurement types (modalities). After formally defining the IC Trojan detection for each side-channel measurement and analyzing the complexity, we devise a new submodular formulation of the problem objective function. Based on the objective function properties, an efficient Trojan detection method with strong approximation and optimality guarantees is introduced. Signal processing methods for calibrating the impact of interchip and intrachip correlations are presented. We define a new sensitivity metric that formally quantifies the impact of modifications to each existing gate that is affected by Trojan. Using the new metric, we compare the Trojan detection capability of different measurement types for static (quiescent) current, dynamic (transient) current, and timing (delay) side-channel measurements. We propose four methods for combining the detection results that are gained from different measurement modalities and show how the sensitivity results can be used for a systematic combining of the detection results. Experimental evaluations on benchmark designs reveal the low-overhead and effectiveness of the new Trojan detection framework and provides a comparison of different detection combining methods.

关键词： change detection algorithms circuit Trojan detection gate-level characterization hardware malware detection hardware security and trust submodular functions timing/power tests

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Unsupervised Land Cover change Estimation Using Region Covariance Estimates

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2019年第3期16卷 347-351页

作者： Olding, W. C. Olivier, J. C. Salmon, B. P. Kleynhans, W. Univ Tasmania Sch Engn Hobart Tas 7005 Australia CSIR Meraka Inst Remote Sensing Res Unit ZA-0081 Pretoria South Africa

In this letter, we demonstrate the utility of estimating a probabilistic model of the underlying seasonal and interannual variations experienced by land cover time series in a given geographical region. Time series that deviate from these trajectories due to the human-induced change appear as outliers and can be detected using their Mahalanobis distance from the mean under the joint distribution of time samples. We apply this model to a collection of pixel time series acquired by the Moderate Resolution Imaging Spectroradiometer platform over Limpopo province, South Africa, for the task of identifying human settlement expansion. For estimation of the time of change, we present a hypothesis testing approach that tests for a decrease in correlation between samples before and after the change. This was found to be highly effective, yielding a mean absolute error of 52 days.

关键词： change detection algorithms covariance matrices density estimation robust algorithm remote sensing time series analysis

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Adaptive Filtering With Reduced Computational Complexity Using SOPOT Arithmetic

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2022年第2期69卷 746-756页

作者： da Silveira Coelho, Luiz Felipe Lovisolo, Lisandro Tcheou, Michel Pompeu Univ Fed Rio de Janeiro COPPE UFRJElect Engn Program PEE Inst Alberto Luiz Coimbra Posgrad & Pesquisa Engn BR-21941972 Rio De Janeiro Brazil Univ Estado Rio de Janeiro UERJ Dept Elect & Telecommun Engn DETEL BR-21945970 Rio De Janeiro Brazil

Implementing finite impulse response (FIR) adaptive filters by employing the sums of signed-powers-of-two (SOPOT) arithmetic may lead to simpler hardware and consequently reduced power consumption. In this paper, one evaluates the effects of SOPOT arithmetic on the adaptive filter's recursion algorithms. The filters' coefficients and algorithms' underlying variables are fully operated using SOPOT arithmetic in the whole iterative process. More specifically, one evaluates convergence rate, numerical stability, and accuracy since using few signed-powers-of-two (SPT) terms propagates numerical errors during the adaptive cycle that may impair the algorithm behavior. The SOPOT approximations are obtained through the technique known as Matching Pursuits with Generalized Bit-Plane (MPGBP) algorithm, with notable cost-performance trade-off and low computational complexity. Results are provided for the Least-Mean-Squares (LMS), the Normalized Least-Mean-Squares (NLMS) and the Recursive-Least-Squares (RLS) algorithms, considering adaptive filters employed for system identification and change detection.

关键词： Approximation algorithms Arithmetic Finite impulse response filters Convergence change detection algorithms Computational complexity Matching pursuit algorithms Sums of signed-powers-of-two adaptive filters SPT-LMS SPT-NLMS SPT-RLS

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A New Coherent Similarity Measure for Temporal Multichannel Scene Characterization

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2012年第7期50卷 2839-2851页

作者： Erten, Esra Reigber, Andreas Ferro-Famil, Laurent Hellwich, Olaf Swiss Fed Inst Technol Chair Earth Observat & Remote Sensing Inst Environm Engn CH-8093 Zurich Switzerland German Aerosp Ctr DLR Microwaves & Radar Inst D-82230 Wessling Germany Univ Rennes 1 Inst Elect & Telecommun Rennes F-35042 Rennes France Tech Univ Berlin Dept Comp Vis & Remote Sensing D-10587 Berlin Germany

This paper proposes a new method for a measure of coherent similarity between temporal multichannel synthetic aperture radar (SAR) images and its implementation to change detection application. The method is based on mutual information (MI) from information theory. The MI measures the amount of information in common between coherent temporal multichannel SAR acquisitions. In order to develop an algorithm for all kinds of SAR images, such as interferometric SAR, polarimetric-interferometric SAR (PolInSAR), and partial PolInSAR, first, the joint density function of temporal multichannel images based on their second-order statistics has been derived. Then, the derived joint density function is used to calculate an analytical expression for the MI between temporal images, which is assumed to be maximal if the temporal images are identical. Although, in this paper, a new coherent similarity measure has analytically been derived for temporal polarimetric SAR images based on complex Wishart process in time, since the mathematical formulation is general, it can equally well be implemented into any kind of multivariate remote sensing data, such as multispectral optical and interferometric images after small continuation. This derived quantity has been implemented for change detection application whose aim is to characterize the temporal behavior of the acquisitions. A comparison between the proposed and the other well-known change detection methods by means of scene characterization is shown, describing the advantages due to the fact that the proposed change detector involves almost every facet of applied change detection.

关键词： change detection algorithms mutual information parameter estimation polarimetric synthetic aperture radar

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A Method for Fault detection and Isolation Based on the Processing of Multiple Diagnostic Indices: Application to Inverter Faults in AC Drives

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2013年第3期62卷 995-1009页

作者： Meinguet, Fabien Sandulescu, Paul Kestelyn, Xavier Semail, Eric Arts & Metiers ParisTech Lab Elect Engn & Power Elect Lille L2EP F-59000 Lille France

A general method for fault detection and isolation (FDI) is proposed and applied to inverter faults in drives of electric vehicles (EVs). This method is based on a change detection algorithm, which allows multiple fault indices (FIs) to be combined to retrieve the most likely state of the drive. The drive topology under study is a six-leg inverter associated with a three-phase open-end winding machine. Due to the inherent fault-tolerant topology, the conventional FIs are no longer effective. Therefore, an analysis of simulations under faulty conditions leads to the derivation of suitable FIs. These are based on the envelope of the phase currents, as well as their instantaneous frequency. Specific operating conditions related to the EV environment are taken into account, such as the flux-weakening region and energy recovery. In these modes of operation, FDI can be affected by uncontrolled currents circulating through the free-wheeling diodes. Finally, the performances of the FDI scheme are evaluated under steady-state and nonstationary conditions through simulations and experimental results.

关键词： AC machines change detection algorithms condition monitoring drives electric vehicles (EVs) fault detection fault diagnosis inverters permanent-magnet machines phase locked loops

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A Deep Learning-Based Cryptocurrency Price Prediction Model That Uses On-Chain Data

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IEEE ACCESS 2022年 10卷 56232-56248页

作者： Kim, Gyeongho Shin, Dong-Hyun Choi, Jae Gyeong Lim, Sunghoon Ulsan Natl Inst Sci & Technol Dept Ind Engn Ulsan 44919 South Korea Ulsan Natl Inst Sci & Technol Dept Biomed Engn Ulsan 44919 South Korea Ulsan Natl Inst Sci & Technol Inst Ind Revolut 4 Ulsan 44919 South Korea

Cryptocurrency has recently attracted substantial interest from investors due to its underlying philosophy of decentralization and transparency. Considering cryptocurrency's volatility and unique characteristics, accurate price prediction is essential for developing successful investment strategies. To this end, the authors of this work propose a novel framework that predicts the price of Bitcoin (BTC), a dominant cryptocurrency. For stable prediction performance in unseen price range, the change point detection technique is employed. In particular, it is used to segment time-series data so that normalization can be separately conducted based on segmentation. In addition, on-chain data, the unique records listed on the blockchain that are inherent in cryptocurrencies, are collected and utilized as input variables to predict prices. Furthermore, this work proposes self-attention-based multiple long short-term memory (SAM-LSTM), which consists of multiple LSTM modules for on-chain variable groups and the attention mechanism, for the prediction model. Experiments with real-world BTC price data and various method setups have proven the proposed framework's effectiveness in BTC price prediction. The results are promising, with the highest MAE, RMSE, MSE, and MAPE values of 0.3462, 0.5035, 0.2536, and 1.3251, respectively.

关键词： Cryptocurrency Blockchains Predictive models Investment Bitcoin Gold Data models Blockchain cryptocurrency Bitcoin deep learning prediction methods change detection algorithms

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UNSUPERVISED SEGMENTATION OF TEXTURED IMAGES BY EDGE-detection IN MULTIDIMENSIONAL FEATURES

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1989年第4期11卷 414-421页

作者： KHOTANZAD, A CHEN, JY CHUNG SHAN INST SCI & TECHNOL TAIPEI TAIWAN

An algorithm for unsupervised texture segmentation is developed that is based on detecting changes in textural characteristics of small local regions. Six features derived from two, two-dimensional, noncausal random field models are used to represent texture. These features contain information about gray-level-value variations in the eight principal directions. An algorithm for automatic selection of the size of the observation windows over which textural activity and change are measured has been developed. Effects of changes in individual features are considered simultaneously by constructing a one-dimensional measure of textural change from them. Edges in this measure correspond to the sought-after textural edges. Experiments results with images containing regions of natural texture show that the algorithm performs very well.

关键词： Image segmentation Image edge detection change detection algorithms Size measurement INSPEC: Controlled Indexing computerised picture processing computerised pattern recognition INSPEC: Non-Controlled Indexing observation windows 2D noncausal random field models computerised picture processing pattern recognition textured images edge detection multidimensional feature unsupervised texture segmentation gray-level-value

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